{"id":"W2264758435","doi":"10.5555/1999416.1999424","title":"Tactical vehicle fleet mix optimization","year":2010,"lang":"en","type":"article","venue":"Summer Computer Simulation Conference","topic":"Quality Function Deployment in Product Design","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Variety (cybernetics); Context (archaeology); Computer science; Vehicle routing problem; Multi-objective optimization; Operations research; Fleet management; Vehicle dynamics; Optimization problem; Mathematical optimization; Engineering; Routing (electronic design automation); Automotive engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002037074,0.001047548,0.0008607174,0.001681865,0.0006043409,0.001795302,0.001291296,0.001185291,0.003551729],"category_scores_gemma":[0.002685064,0.0005613447,0.000987694,0.001630579,0.0008126062,0.001483305,0.001503024,0.001115057,0.0003428727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001651131,"about_ca_system_score_gemma":0.001907657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002808633,"about_ca_topic_score_gemma":0.002706891,"domain_scores_codex":[0.9989123,0.0005577072,0.00003285046,0.0001065673,0.0002698113,0.000120677],"domain_scores_gemma":[0.9992843,0.0004177732,0.0001083896,0.00003821501,0.00011593,0.00003541599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009914421,0.00002796537,0.0002268413,0.00004966632,0.00002260762,0.00002882072,0.00003020714,0.9151961,0.0004696794,0.0707741,0.0003189961,0.01284503],"study_design_scores_gemma":[0.00000620194,0.00002669158,0.0001055096,0.00001898259,0.000007421597,0.00001849988,0.00004188373,0.9617777,0.0003981427,0.03515991,0.002432688,0.000006381413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007177231,0.000268339,0.9858527,0.0001594677,0.00001505127,0.00006526672,0.00005849177,0.00003981522,0.006363595],"genre_scores_gemma":[0.4764621,0.001414966,0.5129659,0.0001058479,0.00006345177,0.0007291854,0.0002404613,0.0001205811,0.007897551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003551729,"threshold_uncertainty_score":0.01197988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05961085550040389,"score_gpt":0.2812105608797371,"score_spread":0.2215997053793332,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}